Polygenic impact of common genetic risk loci for Alzheimer’s disease on cerebral blood flow in young individuals

Abstract

Genome-wide association studies (GWAS) show that many common alleles confer risk for developing Alzheimer’s disease (AD). These risk loci may contribute to MRI alterations in young individuals, preceding the clinical manifestations of AD. Prior evidence identifies vascular dysregulation as the earliest marker of disease progression. However, it remains unclear whether cerebrovascular function (measured via grey-matter cerebral blood flow (gmCBF)) is altered in young individuals with increased AD genetic risk. We establish relationships between gmCBF with APOE and AD polygenic risk score in a young cohort (N = 75; aged: 19–32). Genetic risk was assessed via a) possessing at least one copy of the APOE ɛ4 allele and b) a polygenic risk score (AD-PRS) estimated from AD-GWAS. We observed a reduction in gmCBF in APOE ɛ4 carriers and a negative relationship between AD-PRS and gmCBF. We further found regional reductions in gmCBF in individuals with higher AD-PRS across the frontal cortex (PFWE < 0.05). Our findings suggest that a larger burden of AD common genetic risk alleles is associated with attenuated cerebrovascular function, during young adulthood. These results suggest that cerebral vasculature is a mechanism by which AD risk alleles confer susceptibility.

The multiplex model of the genetics of Alzheimer’s disease

“…Early studies showcase the potential roles of individual GWAS AD SNPs on brain structure and function 127,128 , however recent work now assesses the impact of the cumulative impact of AD risk SNPs through CPRS. These studies have primarily focused on putatively AD susceptible brain regions, such as medial temporal lobe macrostructure (hippocampal formation; amygdala) and other in vivo biomarkers of AD pathology such as Aβ42 deposition [129][130][131][132][133] . Collectively these observations suggest that an excessive burden of AD risk alleles may compromise brain health in individual's years before the onset of clinical symptoms.…”

Polygenic risk for Alzheimer's disease shapes hippocampal scene-selectivity

“…Early evidence already suggests that there are alterations in task-dependent hippocampal cerebrovascular reactivity in young carriers of APOE ɛ4 [57]. Our preliminary data also suggests that AD-PRS may influence resting cerebral blood flow [30], warranting future studies linking AD-PRS to MRI parameters such as the cerebral metabolic rate of oxygen (CMRO2) and oxygen extraction fraction (OEF) to establish the regional, molecular impact of AD-PRS on cerebro-vasculature and/or brain metabolism.…”

“…We chose initially choose a liberal AD-PRS at P T < 5 × 10 −1 , previously been shown to be most predictive of AD in case-control studies [28,29]. In a post-hoc analysis, we create a series of P-threshold across a range of thresholds that have also been shown to associate with AD neuroimaging phenotypes [30,31]. Individual APOE status was determined by the absence/presence of an ɛ4 allele calculated via rs7412 and rs429358.…”

“…In the present study, we combine imaging and genetic data from healthy individuals recruited by the Young Human Connectome Project (YA-HCP) to investigate the combined impact of AD common risk alleles on HC activity as assessed with BOLD during scene encoding in young healthy individuals (aged [22][23][24][25][26][27][28][29][30][31][32][33][34][35]. Based on prior work that evidences hyperactivity in presymptomatic individuals at risk of developing AD [23], we predict a positive association between scene related HC BOLD and genetic risk assayed by AD-PRS (excluding the APOE region) in this young healthy cohort.…”

Improving predictive models for Alzheimer’s disease using GWAS data by incorporating misclassified samples modeling

“…There are many approaches to generate multivariate predictive models from GWAS data [17]. The polygenic risk score (PRS) is the most popular approach and has been applied to AD several times [17][18][19][20][21][22]. PRS is a sum of univariate estimated coefficients; thus, it is easily interpreted.…”